Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations2736
Missing cells680
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory384.9 KiB
Average record size in memory144.0 B

Variable types

DateTime1
Categorical5
Numeric12

Alerts

Ship_Type has 136 (5.0%) missing values Missing
Route_Type has 136 (5.0%) missing values Missing
Engine_Type has 136 (5.0%) missing values Missing
Maintenance_Status has 136 (5.0%) missing values Missing
Weather_Condition has 136 (5.0%) missing values Missing
Speed_Over_Ground_knots has unique values Unique
Engine_Power_kW has unique values Unique
Distance_Traveled_nm has unique values Unique
Draft_meters has unique values Unique
Cargo_Weight_tons has unique values Unique
Operational_Cost_USD has unique values Unique
Revenue_per_Voyage_USD has unique values Unique
Turnaround_Time_hours has unique values Unique
Efficiency_nm_per_kWh has unique values Unique
Seasonal_Impact_Score has unique values Unique
Average_Load_Percentage has unique values Unique

Reproduction

Analysis started2025-02-22 03:17:04.975903
Analysis finished2025-02-22 03:17:11.568601
Duration6.59 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Date
Date

Distinct57
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
Minimum2023-06-04 00:00:00
Maximum2024-06-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-22T00:17:11.609122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T00:17:11.662447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Ship_Type
Categorical

Missing 

Distinct4
Distinct (%)0.2%
Missing136
Missing (%)5.0%
Memory size21.5 KiB
Bulk Carrier
669 
Fish Carrier
653 
Tanker
643 
Container Ship
635 

Length

Max length14
Median length12
Mean length11.004615
Min length6

Characters and Unicode

Total characters28612
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowContainer Ship
2nd rowFish Carrier
3rd rowContainer Ship
4th rowBulk Carrier
5th rowFish Carrier

Common Values

ValueCountFrequency (%)
Bulk Carrier 669
24.5%
Fish Carrier 653
23.9%
Tanker 643
23.5%
Container Ship 635
23.2%
(Missing) 136
 
5.0%

Length

2025-02-22T00:17:11.749318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T00:17:11.779992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
carrier 1322
29.0%
bulk 669
14.7%
fish 653
14.3%
tanker 643
14.1%
container 635
13.9%
ship 635
13.9%

Most occurring characters

ValueCountFrequency (%)
r 5244
18.3%
i 3245
11.3%
e 2600
9.1%
a 2600
9.1%
1957
 
6.8%
C 1957
 
6.8%
n 1913
 
6.7%
k 1312
 
4.6%
h 1288
 
4.5%
B 669
 
2.3%
Other values (9) 5827
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28612
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 5244
18.3%
i 3245
11.3%
e 2600
9.1%
a 2600
9.1%
1957
 
6.8%
C 1957
 
6.8%
n 1913
 
6.7%
k 1312
 
4.6%
h 1288
 
4.5%
B 669
 
2.3%
Other values (9) 5827
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28612
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 5244
18.3%
i 3245
11.3%
e 2600
9.1%
a 2600
9.1%
1957
 
6.8%
C 1957
 
6.8%
n 1913
 
6.7%
k 1312
 
4.6%
h 1288
 
4.5%
B 669
 
2.3%
Other values (9) 5827
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28612
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 5244
18.3%
i 3245
11.3%
e 2600
9.1%
a 2600
9.1%
1957
 
6.8%
C 1957
 
6.8%
n 1913
 
6.7%
k 1312
 
4.6%
h 1288
 
4.5%
B 669
 
2.3%
Other values (9) 5827
20.4%

Route_Type
Categorical

Missing 

Distinct4
Distinct (%)0.2%
Missing136
Missing (%)5.0%
Memory size21.5 KiB
Long-haul
686 
Coastal
650 
Transoceanic
638 
Short-haul
626 

Length

Max length12
Median length10
Mean length9.4769231
Min length7

Characters and Unicode

Total characters24640
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShort-haul
2nd rowLong-haul
3rd rowTransoceanic
4th rowTransoceanic
5th rowLong-haul

Common Values

ValueCountFrequency (%)
Long-haul 686
25.1%
Coastal 650
23.8%
Transoceanic 638
23.3%
Short-haul 626
22.9%
(Missing) 136
 
5.0%

Length

2025-02-22T00:17:11.817123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T00:17:11.842459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
long-haul 686
26.4%
coastal 650
25.0%
transoceanic 638
24.5%
short-haul 626
24.1%

Most occurring characters

ValueCountFrequency (%)
a 3888
15.8%
o 2600
10.6%
n 1962
 
8.0%
l 1962
 
8.0%
h 1938
 
7.9%
- 1312
 
5.3%
u 1312
 
5.3%
s 1288
 
5.2%
t 1276
 
5.2%
c 1276
 
5.2%
Other values (8) 5826
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24640
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3888
15.8%
o 2600
10.6%
n 1962
 
8.0%
l 1962
 
8.0%
h 1938
 
7.9%
- 1312
 
5.3%
u 1312
 
5.3%
s 1288
 
5.2%
t 1276
 
5.2%
c 1276
 
5.2%
Other values (8) 5826
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24640
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3888
15.8%
o 2600
10.6%
n 1962
 
8.0%
l 1962
 
8.0%
h 1938
 
7.9%
- 1312
 
5.3%
u 1312
 
5.3%
s 1288
 
5.2%
t 1276
 
5.2%
c 1276
 
5.2%
Other values (8) 5826
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24640
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3888
15.8%
o 2600
10.6%
n 1962
 
8.0%
l 1962
 
8.0%
h 1938
 
7.9%
- 1312
 
5.3%
u 1312
 
5.3%
s 1288
 
5.2%
t 1276
 
5.2%
c 1276
 
5.2%
Other values (8) 5826
23.6%

Engine_Type
Categorical

Missing 

Distinct3
Distinct (%)0.1%
Missing136
Missing (%)5.0%
Memory size21.5 KiB
Diesel
892 
Steam Turbine
855 
Heavy Fuel Oil (HFO)
853 

Length

Max length20
Median length13
Mean length12.895
Min length6

Characters and Unicode

Total characters33527
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHeavy Fuel Oil (HFO)
2nd rowSteam Turbine
3rd rowDiesel
4th rowSteam Turbine
5th rowDiesel

Common Values

ValueCountFrequency (%)
Diesel 892
32.6%
Steam Turbine 855
31.2%
Heavy Fuel Oil (HFO) 853
31.2%
(Missing) 136
 
5.0%

Length

2025-02-22T00:17:11.879625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T00:17:11.904005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel 892
14.8%
steam 855
14.2%
turbine 855
14.2%
heavy 853
14.2%
fuel 853
14.2%
oil 853
14.2%
hfo 853
14.2%

Most occurring characters

ValueCountFrequency (%)
e 5200
15.5%
3414
 
10.2%
i 2600
 
7.8%
l 2598
 
7.7%
u 1708
 
5.1%
a 1708
 
5.1%
O 1706
 
5.1%
F 1706
 
5.1%
H 1706
 
5.1%
D 892
 
2.7%
Other values (12) 10289
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33527
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5200
15.5%
3414
 
10.2%
i 2600
 
7.8%
l 2598
 
7.7%
u 1708
 
5.1%
a 1708
 
5.1%
O 1706
 
5.1%
F 1706
 
5.1%
H 1706
 
5.1%
D 892
 
2.7%
Other values (12) 10289
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33527
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5200
15.5%
3414
 
10.2%
i 2600
 
7.8%
l 2598
 
7.7%
u 1708
 
5.1%
a 1708
 
5.1%
O 1706
 
5.1%
F 1706
 
5.1%
H 1706
 
5.1%
D 892
 
2.7%
Other values (12) 10289
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33527
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5200
15.5%
3414
 
10.2%
i 2600
 
7.8%
l 2598
 
7.7%
u 1708
 
5.1%
a 1708
 
5.1%
O 1706
 
5.1%
F 1706
 
5.1%
H 1706
 
5.1%
D 892
 
2.7%
Other values (12) 10289
30.7%

Maintenance_Status
Categorical

Missing 

Distinct3
Distinct (%)0.1%
Missing136
Missing (%)5.0%
Memory size21.5 KiB
Good
873 
Fair
867 
Critical
860 

Length

Max length8
Median length4
Mean length5.3230769
Min length4

Characters and Unicode

Total characters13840
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCritical
2nd rowGood
3rd rowFair
4th rowFair
5th rowFair

Common Values

ValueCountFrequency (%)
Good 873
31.9%
Fair 867
31.7%
Critical 860
31.4%
(Missing) 136
 
5.0%

Length

2025-02-22T00:17:11.939822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T00:17:11.966102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
good 873
33.6%
fair 867
33.3%
critical 860
33.1%

Most occurring characters

ValueCountFrequency (%)
i 2587
18.7%
o 1746
12.6%
a 1727
12.5%
r 1727
12.5%
G 873
 
6.3%
d 873
 
6.3%
F 867
 
6.3%
C 860
 
6.2%
t 860
 
6.2%
c 860
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2587
18.7%
o 1746
12.6%
a 1727
12.5%
r 1727
12.5%
G 873
 
6.3%
d 873
 
6.3%
F 867
 
6.3%
C 860
 
6.2%
t 860
 
6.2%
c 860
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2587
18.7%
o 1746
12.6%
a 1727
12.5%
r 1727
12.5%
G 873
 
6.3%
d 873
 
6.3%
F 867
 
6.3%
C 860
 
6.2%
t 860
 
6.2%
c 860
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2587
18.7%
o 1746
12.6%
a 1727
12.5%
r 1727
12.5%
G 873
 
6.3%
d 873
 
6.3%
F 867
 
6.3%
C 860
 
6.2%
t 860
 
6.2%
c 860
 
6.2%

Speed_Over_Ground_knots
Real number (ℝ)

Unique 

Distinct2736
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.602863
Minimum10.009756
Maximum24.997043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2025-02-22T00:17:12.120215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.009756
5-th percentile10.783585
Q113.928452
median17.713757
Q321.284785
95-th percentile24.323684
Maximum24.997043
Range14.987288
Interquartile range (IQR)7.3563329

Descriptive statistics

Standard deviation4.3119787
Coefficient of variation (CV)0.24495894
Kurtosis-1.1743735
Mean17.602863
Median Absolute Deviation (MAD)3.7131639
Skewness-0.034935264
Sum48161.434
Variance18.59316
MonotonicityNot monotonic
2025-02-22T00:17:12.168721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.59755752 1
 
< 0.1%
10.44193169 1
 
< 0.1%
10.57581615 1
 
< 0.1%
10.84020125 1
 
< 0.1%
13.96204894 1
 
< 0.1%
11.98480703 1
 
< 0.1%
21.95547124 1
 
< 0.1%
14.21108032 1
 
< 0.1%
21.89531873 1
 
< 0.1%
19.49780838 1
 
< 0.1%
Other values (2726) 2726
99.6%
ValueCountFrequency (%)
10.00975574 1
< 0.1%
10.02479348 1
< 0.1%
10.03427278 1
< 0.1%
10.05166865 1
< 0.1%
10.05431879 1
< 0.1%
10.05860954 1
< 0.1%
10.05869001 1
< 0.1%
10.06511315 1
< 0.1%
10.09375242 1
< 0.1%
10.09512766 1
< 0.1%
ValueCountFrequency (%)
24.99704335 1
< 0.1%
24.99166138 1
< 0.1%
24.98046287 1
< 0.1%
24.97758162 1
< 0.1%
24.97261048 1
< 0.1%
24.97203105 1
< 0.1%
24.96934059 1
< 0.1%
24.96905653 1
< 0.1%
24.95150378 1
< 0.1%
24.94356056 1
< 0.1%

Engine_Power_kW
Real number (ℝ)

Unique 

Distinct2736
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1757.6109
Minimum501.02522
Maximum2998.7343
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2025-02-22T00:17:12.215548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum501.02522
5-th percentile621.87821
Q11148.1049
median1757.4943
Q32382.5943
95-th percentile2872.358
Maximum2998.7343
Range2497.7091
Interquartile range (IQR)1234.4894

Descriptive statistics

Standard deviation717.00278
Coefficient of variation (CV)0.40794169
Kurtosis-1.1780782
Mean1757.6109
Median Absolute Deviation (MAD)614.67048
Skewness-0.0056108035
Sum4808823.5
Variance514092.98
MonotonicityNot monotonic
2025-02-22T00:17:12.262181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2062.983982 1
 
< 0.1%
2804.380957 1
 
< 0.1%
1432.570693 1
 
< 0.1%
1440.600735 1
 
< 0.1%
2168.721772 1
 
< 0.1%
1013.62077 1
 
< 0.1%
2977.864839 1
 
< 0.1%
1287.333621 1
 
< 0.1%
854.2222305 1
 
< 0.1%
1310.033177 1
 
< 0.1%
Other values (2726) 2726
99.6%
ValueCountFrequency (%)
501.0252196 1
< 0.1%
501.739027 1
< 0.1%
502.1060234 1
< 0.1%
502.5369195 1
< 0.1%
504.4811144 1
< 0.1%
505.5228743 1
< 0.1%
506.0392348 1
< 0.1%
506.2524906 1
< 0.1%
508.5406692 1
< 0.1%
508.8914689 1
< 0.1%
ValueCountFrequency (%)
2998.734329 1
< 0.1%
2998.685375 1
< 0.1%
2998.657159 1
< 0.1%
2997.161001 1
< 0.1%
2995.638592 1
< 0.1%
2995.549644 1
< 0.1%
2995.244509 1
< 0.1%
2994.31394 1
< 0.1%
2993.427741 1
< 0.1%
2993.226478 1
< 0.1%

Distance_Traveled_nm
Real number (ℝ)

Unique 

Distinct2736
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1036.4062
Minimum50.43315
Maximum1998.3371
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2025-02-22T00:17:12.306946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50.43315
5-th percentile144.71938
Q1548.51157
median1037.8161
Q31540.9342
95-th percentile1906.4175
Maximum1998.3371
Range1947.9039
Interquartile range (IQR)992.42259

Descriptive statistics

Standard deviation568.63208
Coefficient of variation (CV)0.54865754
Kurtosis-1.2207552
Mean1036.4062
Median Absolute Deviation (MAD)495.81537
Skewness-0.021635608
Sum2835607.4
Variance323342.44
MonotonicityNot monotonic
2025-02-22T00:17:12.355481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1030.943616 1
 
< 0.1%
1109.910506 1
 
< 0.1%
1567.535844 1
 
< 0.1%
1570.506984 1
 
< 0.1%
1277.706598 1
 
< 0.1%
1531.755737 1
 
< 0.1%
227.2213012 1
 
< 0.1%
868.0980445 1
 
< 0.1%
113.4174387 1
 
< 0.1%
365.292517 1
 
< 0.1%
Other values (2726) 2726
99.6%
ValueCountFrequency (%)
50.43314997 1
< 0.1%
50.52421393 1
< 0.1%
51.60485638 1
< 0.1%
52.28660798 1
< 0.1%
52.36599423 1
< 0.1%
52.56616768 1
< 0.1%
53.0084798 1
< 0.1%
54.67608276 1
< 0.1%
55.46525535 1
< 0.1%
59.25931852 1
< 0.1%
ValueCountFrequency (%)
1998.337057 1
< 0.1%
1998.035422 1
< 0.1%
1997.838275 1
< 0.1%
1996.830088 1
< 0.1%
1996.554335 1
< 0.1%
1996.527348 1
< 0.1%
1995.424969 1
< 0.1%
1995.417244 1
< 0.1%
1995.052155 1
< 0.1%
1993.573031 1
< 0.1%

Draft_meters
Real number (ℝ)

Unique 

Distinct2736
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9291027
Minimum5.0019466
Maximum14.992947
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2025-02-22T00:17:12.401832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.0019466
5-th percentile5.4863496
Q17.4374846
median9.9189653
Q312.413149
95-th percentile14.447326
Maximum14.992947
Range9.9910009
Interquartile range (IQR)4.9756642

Descriptive statistics

Standard deviation2.8764229
Coefficient of variation (CV)0.28969616
Kurtosis-1.201138
Mean9.9291027
Median Absolute Deviation (MAD)2.4831328
Skewness0.021333176
Sum27166.025
Variance8.2738089
MonotonicityNot monotonic
2025-02-22T00:17:12.447158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.13228407 1
 
< 0.1%
7.650915101 1
 
< 0.1%
12.20257083 1
 
< 0.1%
5.022887815 1
 
< 0.1%
8.370894148 1
 
< 0.1%
9.298829903 1
 
< 0.1%
7.451510087 1
 
< 0.1%
6.122657426 1
 
< 0.1%
6.934268992 1
 
< 0.1%
10.81497567 1
 
< 0.1%
Other values (2726) 2726
99.6%
ValueCountFrequency (%)
5.001946569 1
< 0.1%
5.0031111 1
< 0.1%
5.012140971 1
< 0.1%
5.012789883 1
< 0.1%
5.017875204 1
< 0.1%
5.022887815 1
< 0.1%
5.025441293 1
< 0.1%
5.036127946 1
< 0.1%
5.045122812 1
< 0.1%
5.046244571 1
< 0.1%
ValueCountFrequency (%)
14.99294749 1
< 0.1%
14.99151728 1
< 0.1%
14.98990483 1
< 0.1%
14.9898178 1
< 0.1%
14.98810409 1
< 0.1%
14.98623989 1
< 0.1%
14.98393567 1
< 0.1%
14.97944363 1
< 0.1%
14.96654514 1
< 0.1%
14.9496676 1
< 0.1%

Weather_Condition
Categorical

Missing 

Distinct3
Distinct (%)0.1%
Missing136
Missing (%)5.0%
Memory size21.5 KiB
Calm
893 
Moderate
891 
Rough
816 

Length

Max length8
Median length5
Mean length5.6846154
Min length4

Characters and Unicode

Total characters14780
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerate
2nd rowRough
3rd rowModerate
4th rowModerate
5th rowModerate

Common Values

ValueCountFrequency (%)
Calm 893
32.6%
Moderate 891
32.6%
Rough 816
29.8%
(Missing) 136
 
5.0%

Length

2025-02-22T00:17:12.490640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T00:17:12.516826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
calm 893
34.3%
moderate 891
34.3%
rough 816
31.4%

Most occurring characters

ValueCountFrequency (%)
a 1784
12.1%
e 1782
12.1%
o 1707
11.5%
C 893
 
6.0%
l 893
 
6.0%
m 893
 
6.0%
M 891
 
6.0%
d 891
 
6.0%
r 891
 
6.0%
t 891
 
6.0%
Other values (4) 3264
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1784
12.1%
e 1782
12.1%
o 1707
11.5%
C 893
 
6.0%
l 893
 
6.0%
m 893
 
6.0%
M 891
 
6.0%
d 891
 
6.0%
r 891
 
6.0%
t 891
 
6.0%
Other values (4) 3264
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1784
12.1%
e 1782
12.1%
o 1707
11.5%
C 893
 
6.0%
l 893
 
6.0%
m 893
 
6.0%
M 891
 
6.0%
d 891
 
6.0%
r 891
 
6.0%
t 891
 
6.0%
Other values (4) 3264
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1784
12.1%
e 1782
12.1%
o 1707
11.5%
C 893
 
6.0%
l 893
 
6.0%
m 893
 
6.0%
M 891
 
6.0%
d 891
 
6.0%
r 891
 
6.0%
t 891
 
6.0%
Other values (4) 3264
22.1%

Cargo_Weight_tons
Real number (ℝ)

Unique 

Distinct2736
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1032.5733
Minimum50.229624
Maximum1999.1267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2025-02-22T00:17:12.552044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50.229624
5-th percentile159.17821
Q1553.98363
median1043.2075
Q31527.7224
95-th percentile1903.4804
Maximum1999.1267
Range1948.8971
Interquartile range (IQR)973.73876

Descriptive statistics

Standard deviation558.6975
Coefficient of variation (CV)0.54107299
Kurtosis-1.1945623
Mean1032.5733
Median Absolute Deviation (MAD)486.13561
Skewness-0.013293242
Sum2825120.5
Variance312142.9
MonotonicityNot monotonic
2025-02-22T00:17:12.603687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1959.017882 1
 
< 0.1%
332.9380341 1
 
< 0.1%
1912.175032 1
 
< 0.1%
1484.648064 1
 
< 0.1%
1243.623945 1
 
< 0.1%
1095.802939 1
 
< 0.1%
299.8222692 1
 
< 0.1%
1481.616929 1
 
< 0.1%
1146.101766 1
 
< 0.1%
622.9554194 1
 
< 0.1%
Other values (2726) 2726
99.6%
ValueCountFrequency (%)
50.22962415 1
< 0.1%
51.71466129 1
< 0.1%
52.29054651 1
< 0.1%
52.64089785 1
< 0.1%
52.98885103 1
< 0.1%
53.5165531 1
< 0.1%
53.89624585 1
< 0.1%
54.18555276 1
< 0.1%
54.31473285 1
< 0.1%
54.81417313 1
< 0.1%
ValueCountFrequency (%)
1999.126697 1
< 0.1%
1998.248109 1
< 0.1%
1997.573054 1
< 0.1%
1997.029315 1
< 0.1%
1996.6611 1
< 0.1%
1995.377905 1
< 0.1%
1994.949074 1
< 0.1%
1992.897833 1
< 0.1%
1992.488382 1
< 0.1%
1992.298745 1
< 0.1%

Operational_Cost_USD
Real number (ℝ)

Unique 

Distinct2736
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean255143.34
Minimum10092.306
Maximum499734.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2025-02-22T00:17:12.651640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10092.306
5-th percentile37279.765
Q1131293.38
median257157.65
Q3381796.93
95-th percentile473606.76
Maximum499734.87
Range489642.56
Interquartile range (IQR)250503.55

Descriptive statistics

Standard deviation140890.48
Coefficient of variation (CV)0.55220128
Kurtosis-1.2181059
Mean255143.34
Median Absolute Deviation (MAD)125588.27
Skewness0.001997241
Sum6.9807219 × 108
Variance1.9850128 × 1010
MonotonicityNot monotonic
2025-02-22T00:17:12.698791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
483832.3545 1
 
< 0.1%
147717.5999 1
 
< 0.1%
38483.06542 1
 
< 0.1%
114975.0262 1
 
< 0.1%
477311.8429 1
 
< 0.1%
75749.26807 1
 
< 0.1%
310416.3055 1
 
< 0.1%
185277.5041 1
 
< 0.1%
226846.454 1
 
< 0.1%
252072.9688 1
 
< 0.1%
Other values (2726) 2726
99.6%
ValueCountFrequency (%)
10092.30632 1
< 0.1%
10097.43966 1
< 0.1%
10189.0047 1
< 0.1%
10225.16338 1
< 0.1%
10411.85836 1
< 0.1%
10814.50537 1
< 0.1%
10836.03101 1
< 0.1%
11088.96421 1
< 0.1%
11170.78683 1
< 0.1%
11376.00561 1
< 0.1%
ValueCountFrequency (%)
499734.8679 1
< 0.1%
499710.9059 1
< 0.1%
499224.5613 1
< 0.1%
499125.2663 1
< 0.1%
499012.8062 1
< 0.1%
498989.085 1
< 0.1%
498862.2162 1
< 0.1%
498671.1088 1
< 0.1%
498660.3728 1
< 0.1%
498593.7885 1
< 0.1%

Revenue_per_Voyage_USD
Real number (ℝ)

Unique 

Distinct2736
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521362.06
Minimum50351.814
Maximum999916.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2025-02-22T00:17:12.742566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50351.814
5-th percentile97215.494
Q1290346.39
median520176.93
Q3750072.79
95-th percentile948359.91
Maximum999916.7
Range949564.88
Interquartile range (IQR)459726.4

Descriptive statistics

Standard deviation271211.63
Coefficient of variation (CV)0.52019824
Kurtosis-1.1644975
Mean521362.06
Median Absolute Deviation (MAD)229949.09
Skewness0.011941106
Sum1.4264466 × 109
Variance7.3555746 × 1010
MonotonicityNot monotonic
2025-02-22T00:17:12.788266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
292183.2731 1
 
< 0.1%
939813.7448 1
 
< 0.1%
132043.1608 1
 
< 0.1%
201884.9988 1
 
< 0.1%
511617.7855 1
 
< 0.1%
859718.2908 1
 
< 0.1%
132052.5958 1
 
< 0.1%
726445.8799 1
 
< 0.1%
310474.1295 1
 
< 0.1%
591335.1208 1
 
< 0.1%
Other values (2726) 2726
99.6%
ValueCountFrequency (%)
50351.81445 1
< 0.1%
50831.10923 1
< 0.1%
50837.96304 1
< 0.1%
50854.61848 1
< 0.1%
50897.72713 1
< 0.1%
50956.29853 1
< 0.1%
51396.88834 1
< 0.1%
51708.40843 1
< 0.1%
52265.57725 1
< 0.1%
52509.06492 1
< 0.1%
ValueCountFrequency (%)
999916.6961 1
< 0.1%
999811.9278 1
< 0.1%
999805.8479 1
< 0.1%
999423.8637 1
< 0.1%
998955.2577 1
< 0.1%
998571.109 1
< 0.1%
998456.068 1
< 0.1%
997940.6462 1
< 0.1%
997830.742 1
< 0.1%
997665.5532 1
< 0.1%

Turnaround_Time_hours
Real number (ℝ)

Unique 

Distinct2736
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.747536
Minimum12.019909
Maximum71.972415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2025-02-22T00:17:12.829048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12.019909
5-th percentile14.711279
Q126.173537
median41.585188
Q357.363922
95-th percentile69.149258
Maximum71.972415
Range59.952506
Interquartile range (IQR)31.190385

Descriptive statistics

Standard deviation17.63313
Coefficient of variation (CV)0.42237536
Kurtosis-1.2506455
Mean41.747536
Median Absolute Deviation (MAD)15.504839
Skewness0.017989716
Sum114221.26
Variance310.92729
MonotonicityNot monotonic
2025-02-22T00:17:12.876366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.86707716 1
 
< 0.1%
46.49729793 1
 
< 0.1%
65.86922822 1
 
< 0.1%
64.40228633 1
 
< 0.1%
64.15725946 1
 
< 0.1%
59.4812252 1
 
< 0.1%
31.00253992 1
 
< 0.1%
20.30519184 1
 
< 0.1%
41.87641778 1
 
< 0.1%
31.56021466 1
 
< 0.1%
Other values (2726) 2726
99.6%
ValueCountFrequency (%)
12.01990927 1
< 0.1%
12.05885716 1
< 0.1%
12.06935592 1
< 0.1%
12.09100195 1
< 0.1%
12.09410086 1
< 0.1%
12.11827517 1
< 0.1%
12.12210172 1
< 0.1%
12.12987746 1
< 0.1%
12.143355 1
< 0.1%
12.15215257 1
< 0.1%
ValueCountFrequency (%)
71.9724153 1
< 0.1%
71.96212537 1
< 0.1%
71.93544219 1
< 0.1%
71.92329125 1
< 0.1%
71.91612694 1
< 0.1%
71.90527086 1
< 0.1%
71.89250154 1
< 0.1%
71.87739077 1
< 0.1%
71.82280767 1
< 0.1%
71.81472451 1
< 0.1%

Efficiency_nm_per_kWh
Real number (ℝ)

Unique 

Distinct2736
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.79865557
Minimum0.10021133
Maximum1.4992594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2025-02-22T00:17:12.920676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.10021133
5-th percentile0.17347777
Q10.46359171
median0.78987657
Q31.1474257
95-th percentile1.434641
Maximum1.4992594
Range1.3990481
Interquartile range (IQR)0.68383399

Descriptive statistics

Standard deviation0.4035898
Coefficient of variation (CV)0.50533648
Kurtosis-1.1912393
Mean0.79865557
Median Absolute Deviation (MAD)0.34486969
Skewness0.025919043
Sum2185.1216
Variance0.16288472
MonotonicityNot monotonic
2025-02-22T00:17:12.966866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.4551789 1
 
< 0.1%
0.9861425981 1
 
< 0.1%
0.772351124 1
 
< 0.1%
0.4178699353 1
 
< 0.1%
0.927477275 1
 
< 0.1%
0.367177484 1
 
< 0.1%
0.5562475999 1
 
< 0.1%
0.6841383572 1
 
< 0.1%
1.462818961 1
 
< 0.1%
1.137178562 1
 
< 0.1%
Other values (2726) 2726
99.6%
ValueCountFrequency (%)
0.1002113328 1
< 0.1%
0.1005826405 1
< 0.1%
0.1009433907 1
< 0.1%
0.1014658138 1
< 0.1%
0.1018720769 1
< 0.1%
0.1021915107 1
< 0.1%
0.1022800203 1
< 0.1%
0.1023155584 1
< 0.1%
0.1031462166 1
< 0.1%
0.1047318649 1
< 0.1%
ValueCountFrequency (%)
1.499259399 1
< 0.1%
1.499024329 1
< 0.1%
1.498191198 1
< 0.1%
1.49702466 1
< 0.1%
1.496668964 1
< 0.1%
1.496387397 1
< 0.1%
1.495445197 1
< 0.1%
1.49519813 1
< 0.1%
1.494392935 1
< 0.1%
1.492314088 1
< 0.1%

Seasonal_Impact_Score
Real number (ℝ)

Unique 

Distinct2736
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.003816
Minimum0.50000439
Maximum1.4992236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2025-02-22T00:17:13.012130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.50000439
5-th percentile0.55318527
Q10.75803264
median1.009009
Q31.252808
95-th percentile1.4468935
Maximum1.4992236
Range0.99921922
Interquartile range (IQR)0.4947754

Descriptive statistics

Standard deviation0.28825071
Coefficient of variation (CV)0.28715491
Kurtosis-1.2019198
Mean1.003816
Median Absolute Deviation (MAD)0.24775193
Skewness-0.01875573
Sum2746.4407
Variance0.08308847
MonotonicityNot monotonic
2025-02-22T00:17:13.057816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.415653291 1
 
< 0.1%
1.108503122 1
 
< 0.1%
1.165680416 1
 
< 0.1%
0.8671223961 1
 
< 0.1%
0.786892167 1
 
< 0.1%
0.84974593 1
 
< 0.1%
0.8441641364 1
 
< 0.1%
1.384142419 1
 
< 0.1%
1.049823421 1
 
< 0.1%
1.402578096 1
 
< 0.1%
Other values (2726) 2726
99.6%
ValueCountFrequency (%)
0.5000043873 1
< 0.1%
0.5004943094 1
< 0.1%
0.501086296 1
< 0.1%
0.5016151303 1
< 0.1%
0.5016344365 1
< 0.1%
0.5016467783 1
< 0.1%
0.5016932521 1
< 0.1%
0.501921978 1
< 0.1%
0.5023937687 1
< 0.1%
0.5025606864 1
< 0.1%
ValueCountFrequency (%)
1.499223608 1
< 0.1%
1.499133796 1
< 0.1%
1.49857856 1
< 0.1%
1.49821131 1
< 0.1%
1.497884289 1
< 0.1%
1.497731838 1
< 0.1%
1.496682806 1
< 0.1%
1.49657237 1
< 0.1%
1.496469634 1
< 0.1%
1.496420532 1
< 0.1%

Weekly_Voyage_Count
Real number (ℝ)

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9148392
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2025-02-22T00:17:13.090469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5976475
Coefficient of variation (CV)0.52853153
Kurtosis-1.2438219
Mean4.9148392
Median Absolute Deviation (MAD)2
Skewness0.024583238
Sum13447
Variance6.7477724
MonotonicityNot monotonic
2025-02-22T00:17:13.121670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 338
12.4%
4 316
11.5%
8 313
11.4%
2 311
11.4%
5 305
11.1%
7 300
11.0%
3 289
10.6%
6 283
10.3%
9 281
10.3%
ValueCountFrequency (%)
1 338
12.4%
2 311
11.4%
3 289
10.6%
4 316
11.5%
5 305
11.1%
6 283
10.3%
7 300
11.0%
8 313
11.4%
9 281
10.3%
ValueCountFrequency (%)
9 281
10.3%
8 313
11.4%
7 300
11.0%
6 283
10.3%
5 305
11.1%
4 316
11.5%
3 289
10.6%
2 311
11.4%
1 338
12.4%

Average_Load_Percentage
Real number (ℝ)

Unique 

Distinct2736
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.219222
Minimum50.012005
Maximum99.999643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2025-02-22T00:17:13.161553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50.012005
5-th percentile52.449498
Q162.703724
median75.504709
Q387.721205
95-th percentile97.606276
Maximum99.999643
Range49.987638
Interquartile range (IQR)25.017481

Descriptive statistics

Standard deviation14.510168
Coefficient of variation (CV)0.19290505
Kurtosis-1.2040798
Mean75.219222
Median Absolute Deviation (MAD)12.557815
Skewness-0.017839902
Sum205799.79
Variance210.54497
MonotonicityNot monotonic
2025-02-22T00:17:13.204915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93.76924947 1
 
< 0.1%
87.15115582 1
 
< 0.1%
92.97870325 1
 
< 0.1%
71.95615041 1
 
< 0.1%
53.43177688 1
 
< 0.1%
52.33694135 1
 
< 0.1%
54.7033037 1
 
< 0.1%
63.70162082 1
 
< 0.1%
58.53286932 1
 
< 0.1%
62.49138295 1
 
< 0.1%
Other values (2726) 2726
99.6%
ValueCountFrequency (%)
50.01200505 1
< 0.1%
50.02268651 1
< 0.1%
50.03266556 1
< 0.1%
50.0413115 1
< 0.1%
50.04477196 1
< 0.1%
50.05283687 1
< 0.1%
50.08049284 1
< 0.1%
50.11518711 1
< 0.1%
50.1617757 1
< 0.1%
50.20530534 1
< 0.1%
ValueCountFrequency (%)
99.99964331 1
< 0.1%
99.99078505 1
< 0.1%
99.95222028 1
< 0.1%
99.94731599 1
< 0.1%
99.86410119 1
< 0.1%
99.8510829 1
< 0.1%
99.84575197 1
< 0.1%
99.83572831 1
< 0.1%
99.83286841 1
< 0.1%
99.80138488 1
< 0.1%

Interactions

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2025-02-22T00:17:05.407229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T00:17:05.993016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T00:17:06.472308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T00:17:07.072444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T00:17:07.512566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T00:17:08.019105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T00:17:08.540379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T00:17:08.977382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-22T00:17:07.981501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T00:17:08.504705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T00:17:08.941352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T00:17:09.394354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T00:17:09.852255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T00:17:10.417785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T00:17:10.883228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-22T00:17:13.245989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Average_Load_PercentageCargo_Weight_tonsDistance_Traveled_nmDraft_metersEfficiency_nm_per_kWhEngine_Power_kWEngine_TypeMaintenance_StatusOperational_Cost_USDRevenue_per_Voyage_USDRoute_TypeSeasonal_Impact_ScoreShip_TypeSpeed_Over_Ground_knotsTurnaround_Time_hoursWeather_ConditionWeekly_Voyage_Count
Average_Load_Percentage1.0000.027-0.005-0.012-0.0110.0240.0400.000-0.0190.0090.000-0.0310.012-0.0220.0200.042-0.007
Cargo_Weight_tons0.0271.0000.0140.0070.006-0.0120.0000.000-0.0360.0160.000-0.0270.021-0.004-0.0310.000-0.001
Distance_Traveled_nm-0.0050.0141.000-0.0110.002-0.0090.0000.0250.0020.0400.0160.0170.000-0.011-0.0260.0000.044
Draft_meters-0.0120.007-0.0111.000-0.0180.0080.0000.0000.0040.0010.019-0.0020.000-0.015-0.0290.0000.013
Efficiency_nm_per_kWh-0.0110.0060.002-0.0181.000-0.0180.0250.0000.0030.0160.000-0.0050.0270.0190.0090.069-0.005
Engine_Power_kW0.024-0.012-0.0090.008-0.0181.0000.0000.000-0.012-0.0110.017-0.0130.000-0.004-0.0330.0310.037
Engine_Type0.0400.0000.0000.0000.0250.0001.0000.0260.0000.0000.0000.0220.0000.0420.0000.0000.000
Maintenance_Status0.0000.0000.0250.0000.0000.0000.0261.0000.0000.0360.0000.0000.0090.0000.0000.0300.000
Operational_Cost_USD-0.019-0.0360.0020.0040.003-0.0120.0000.0001.0000.0240.0100.0210.000-0.036-0.0030.0000.008
Revenue_per_Voyage_USD0.0090.0160.0400.0010.016-0.0110.0000.0360.0241.0000.0000.0190.0190.002-0.0280.0000.011
Route_Type0.0000.0000.0160.0190.0000.0170.0000.0000.0100.0001.0000.0000.0040.0370.0000.0170.000
Seasonal_Impact_Score-0.031-0.0270.017-0.002-0.005-0.0130.0220.0000.0210.0190.0001.0000.0000.0280.0090.0000.008
Ship_Type0.0120.0210.0000.0000.0270.0000.0000.0090.0000.0190.0040.0001.0000.0180.0000.0180.015
Speed_Over_Ground_knots-0.022-0.004-0.011-0.0150.019-0.0040.0420.000-0.0360.0020.0370.0280.0181.000-0.0050.0000.019
Turnaround_Time_hours0.020-0.031-0.026-0.0290.009-0.0330.0000.000-0.003-0.0280.0000.0090.000-0.0051.0000.000-0.012
Weather_Condition0.0420.0000.0000.0000.0690.0310.0000.0300.0000.0000.0170.0000.0180.0000.0001.0000.030
Weekly_Voyage_Count-0.007-0.0010.0440.013-0.0050.0370.0000.0000.0080.0110.0000.0080.0150.019-0.0120.0301.000

Missing values

2025-02-22T00:17:11.398436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-22T00:17:11.463774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-22T00:17:11.537924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateShip_TypeRoute_TypeEngine_TypeMaintenance_StatusSpeed_Over_Ground_knotsEngine_Power_kWDistance_Traveled_nmDraft_metersWeather_ConditionCargo_Weight_tonsOperational_Cost_USDRevenue_per_Voyage_USDTurnaround_Time_hoursEfficiency_nm_per_kWhSeasonal_Impact_ScoreWeekly_Voyage_CountAverage_Load_Percentage
02023-06-04Container ShipNaNHeavy Fuel Oil (HFO)Critical12.5975582062.9839821030.94361614.132284Moderate1959.017882483832.354540292183.27310425.8670771.4551791.415653193.769249
12023-06-11Fish CarrierShort-haulSteam TurbineGood10.3875801796.0574151060.48638214.653083Rough162.394712483388.000509883765.78736063.2481960.2903610.885648693.895372
22023-06-18Container ShipLong-haulDieselFair20.7497471648.556685658.8741447.199261Moderate178.040917448543.404044394018.74690449.4181500.4995951.405813996.218244
32023-06-25Bulk CarrierTransoceanicSteam TurbineFair21.055102915.2617951126.82251911.789063Moderate1737.385346261349.60544987551.37517522.4091100.7029061.370704166.193698
42023-07-02Fish CarrierTransoceanicDieselFair13.7427771089.7218031445.2811599.727833Moderate260.595103287718.375160676121.45963264.1582311.3313430.583383880.008581
52023-07-09Fish CarrierLong-haulHeavy Fuel Oil (HFO)Fair18.6161962171.646567723.30421514.916320Rough1912.509751184569.045384776698.35484947.4761551.3702061.437725753.174898
62023-07-16Fish CarrierTransoceanicHeavy Fuel Oil (HFO)Critical20.4331192505.043509270.1185888.455264Rough1191.436412434449.263514739431.82539029.0404201.1203890.602932358.508635
72023-07-23Container ShipShort-haulDieselCritical23.498048814.8064521717.3284139.283780Moderate318.449265348380.608232462211.40219952.4786000.6959751.408663694.591972
82023-07-30NaNCoastalHeavy Fuel Oil (HFO)Good17.3093621179.018805429.5643136.002358Moderate1953.447929346071.519942448682.86412814.4518200.9774480.764914850.247060
92023-08-06Container ShipLong-haulDieselFair23.2271012685.4016541588.79228211.721261Calm1843.438252149790.209291615230.85749163.7427610.4406761.435569279.451330
DateShip_TypeRoute_TypeEngine_TypeMaintenance_StatusSpeed_Over_Ground_knotsEngine_Power_kWDistance_Traveled_nmDraft_metersWeather_ConditionCargo_Weight_tonsOperational_Cost_USDRevenue_per_Voyage_USDTurnaround_Time_hoursEfficiency_nm_per_kWhSeasonal_Impact_ScoreWeekly_Voyage_CountAverage_Load_Percentage
27262024-04-28Fish CarrierCoastalSteam TurbineCritical11.0259582497.187954812.4922799.094104Rough722.356381308153.450057986482.40590021.2960110.8166831.136584578.671414
27272024-05-05Fish CarrierTransoceanicSteam TurbineGood17.2433431288.183329160.1036225.336910Rough1705.39089410189.004697297707.74433944.7382510.8939211.044378159.661171
27282024-05-12Container ShipTransoceanicDieselGood15.6498382537.8083751032.67995313.349315Rough853.937920393388.586706396539.44726553.5404280.8144711.311372394.829144
27292024-05-19Container ShipLong-haulHeavy Fuel Oil (HFO)Good24.2574021813.474360147.96429610.378736Moderate1450.181358266359.224719820652.03892334.4336550.7081721.352973751.258697
27302024-05-26TankerLong-haulDieselCritical15.6492071662.4265571175.94272412.564046Rough451.670997393827.995941567287.16965322.2816380.3560311.079178292.926949
27312024-06-02TankerShort-haulHeavy Fuel Oil (HFO)Good11.6079972918.395972239.99035913.700906Moderate318.111891237975.067292731584.32292147.1523371.0002651.284895374.813114
27322024-06-09Bulk CarrierShort-haulHeavy Fuel Oil (HFO)Good13.8527982161.282358831.35565314.612775NaN218.30900221029.021721374365.37093064.3259160.6534740.891085284.595155
27332024-06-16Container ShipShort-haulSteam TurbineCritical16.8137131343.6080061376.4606229.306518NaN1630.64641978883.312529234120.36505253.5510900.5941690.725404680.975269
27342024-06-23TankerTransoceanicHeavy Fuel Oil (HFO)Good23.1326432028.143572619.2363406.623856Moderate153.44196525241.550250799713.73721114.3355170.8956700.902960292.853622
27352024-06-30Fish CarrierCoastalSteam TurbineFair11.5279872928.5881081930.23577914.187652Moderate712.99789455163.241668382208.02140563.8862560.8253491.289204266.190613